Delete finetune.py

finetune.py

@@ -1,60 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
-from peft import LoraConfig, get_peft_model
-from datasets import load_dataset
-
-# Load model and tokenizer
-model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Use 4-bit quantization to save RAM
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    load_in_4bit=True,  # ✅ Reduce VRAM usage
-    device_map="cpu"  # ✅ Force CPU usage
-)
-
-# Apply LoRA configuration
-lora_config = LoraConfig(
-    r=8,  # Low-rank dimension (small)
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM"  # For TinyLlama (Chat models)
-)
-
-model = get_peft_model(model, lora_config)
-
-# Load dataset (Example: "Hello world" text)
-dataset = load_dataset("Abirate/english_quotes", split="train[:1000]")
-
-def tokenize_function(examples):
-    return tokenizer(examples["quote"], padding="max_length", truncation=True, max_length=128)
-
-tokenized_datasets = dataset.map(tokenize_function, batched=True)
-
-# Training arguments
-training_args = TrainingArguments(
-    output_dir="./tinyllama_lora",
-    per_device_train_batch_size=2,  # ✅ Small batch size for CPU
-    num_train_epochs=1,
-    save_steps=10,
-    logging_steps=10,
-    optim="adamw_torch",  # ✅ Better optimizer
-    save_total_limit=1,
-)
-
-# Trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets
-)
-
-# Start training
-trainer.train()
-
-# Save the fine-tuned model
-model.save_pretrained("tinyllama-lora-finetuned")
-tokenizer.save_pretrained("tinyllama-lora-finetuned")
-print("✅ Fine-tuning complete! Model saved.")